🦠 virus-evo-simulation

virus-evo-simulation is a visual, real-time simulation of virus spread and mutation, written in C with SDL2. The project models the behavior of viral strains inside a cellular automaton: infection, death, revival, and evolution through random mutations. It features a multithreaded engine and a graphical interface powered by SDL2.

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⚙️ Features

• Cellular automaton simulation with 3 states: Healthy, Infected, Dead
• Dynamic mutations that generate new virus strains with unique properties and visuals
• Dead cells can be revived by nearby healthy cells
• Up to 10 concurrent strains on the grid
• Mutation history log with descriptive messages
• Real-time graphics with SDL2 and TTF text rendering
• Multithreaded simulation using pthread and mutex protection for data safety

🛠️ Build & Run (Linux)

Prerequisites

Install required libraries (example for Debian/Ubuntu-based systems):

sudo apt install libsdl2-dev libsdl2-ttf-dev build-essential

Build the project

make

Run the simulation

./virus_sim

Clean compiled files

make clean

📁 Project Structure

.
├── include/             # Header files
├── src/
│   ├── main.c           # Entry point
│   ├── simulation/      # Simulation logic
│   │   ├── core.c       # Main loop and control
│   │   ├── grid.c       # Grid and cell operations
│   │   └── mutation.c   # Mutation logic
│   └── rendering/       # SDL rendering engine
│       └── render.c     # Drawing cells and mutation effects
├── Makefile             # Build script
└── README.md            # Project documentation

🧬 Simulation Logic

• Each cell can be in one of three states: HEALTHY, INFECTED, or DEAD
• Infected cells either recover or die, based on the virus strain's parameters
• Dead cells have a chance to be revived
• Each virus strain includes:
  • A unique color
  • Infection, death, and revival probabilities
  • Recovery time
• When a strain infects enough cells, it mutates, creating a new strain with modified properties and color
• Mutations trigger a visual effect and are recorded in a mutation log

🤝 Contributions Welcome

Pull requests are highly appreciated! If you have ideas for improvements — like enhanced mutation systems, charts, save/load functionality, or performance optimizations — join the development!

If you like this project, consider ⭐️ starring it on GitHub!